Optimum Delivery Plan for Trucks in Urban Area under Environmental Restrictions Using Probedata

本研究旨在通过安装在卡车上的一套探测设备(包括GPS和数据记录器)收集的探测数据来揭示送货活动的基本特征。利用这些探测数据，我们建立了数学模型，以制定环境限制下的卡车交付计划。从降低卡车运营成本和改善城市环境的角度，提出了不同卡车的配送方案，并进行了评价。本研究的主要研究成果如下：首先，考虑基于探测数据得到的出行时间和装卸时间的不确定性，建立了确定最佳发车时间的数学模型，以确定指定的发货时间和选择最优发货路线。推导了影响货车在路网上路径选择的一些因素，建立了以显性因素为解释变量的非集合体路径选择模型。其次，针对不同的货车(送货上门服务货车和货运集装箱拖车)制定了两种配送方案。1)提出了货运OD需求下的送货卡车配送中心配置方案，并从提高送货服务效率和减少对环境的负面影响两方面进行了评价。2)利用我们的路径选择模型，我们估计了收费政策对卡车(挂车)行驶路线从城市地区的高速公路、环境脆弱地区改为沿海地区的高速公路以改善路边环境的影响。

This study aims to reveal the basic characteristics of delivery activities based on probe date which were gathered by a set of probe equipment (comprising GPS and data logger) mounted to trucks. With these probe data, we formulated mathematical models to lay out delivery plans for trucks under environmental restrictions. Then some delivery plans for different trucks were proposed and evaluated from the viewpoints of both reduction in truck operation cost and improvement of urban environment. The main findings of this study are summarized as follows:First, we built mathematical models for deciding optimum departure time to make the designated delivery time and choosing optimum delivery route by considering the uncertainty of travel time and loading/unloading time obtained based on probe data. We also deducted some factors influencing route choice by trucks on road networks and developed disaggregate route choice models using revealed factors as explanatory variables.Second, we made two types of delivery plans for different trucks (home delivery service trucks and freight container trailer trucks). 1) We proposed allocation plans of distribution centers for home delivery trucks under the cargo OD demand, and evaluated them from improvement of efficiency of truck delivery service as well as reduction in negative environmental impacts. 2) We estimated the effects of toll policy on changing trucks' (trailer trucks) running routes from highways in urban area, environmentally vulnerable area, to expressway in coastal area to improve roadside environment, using our route choice models.